A. Duniewicz et al., “The Sound Map of Białystok − From monophonic to immersive audio repository of urban soundscapes,” in Proc. Express Paper, May 2025, Paper 353. [Online]. Available: https://aes.org/publications/elibrary-page/?id=22904
Duniewicz A, Bagińska K, Borys K, Antoniuk P, Wójcik K, Mrozek I, Zielinski SK. The Sound Map of Białystok − From monophonic to immersive audio repository of urban soundscapes. In: Express Paper. Audio Engineering Society; 2025. Paper 353. Available from: https://aes.org/publications/elibrary-page/?id=22904
@inproceedings{Duniewicz2025_22904,
author = {Duniewicz, Agnieszka and Bagińska, Klaudia and Borys, Kamila and Antoniuk, Pawel and Wójcik, Karol and Mrozek, Ireneusz and Zielinski, Slawomir K.},
title = {{The Sound Map of Białystok − From monophonic to immersive audio repository of urban soundscapes}},
note = {Paper 353},
year = {2025},
month = may,
publisher = {Audio Engineering Society},
url = {https://aes.org/publications/elibrary-page/?id=22904}
}
TY - CPAPER
TI - The Sound Map of Białystok − From monophonic to immersive audio repository of urban soundscapes
AU - Duniewicz, Agnieszka
AU - Bagińska, Klaudia
AU - Borys, Kamila
AU - Antoniuk, Pawel
AU - Wójcik, Karol
AU - Mrozek, Ireneusz
AU - Zielinski, Slawomir K.
M1 - Paper 353
PY - 2025
DA - 2025/05/12
UR - https://aes.org/publications/elibrary-page/?id=22904
PB - Audio Engineering Society
LA - en
AB - This paper presents an ongoing project that aims to document the urban soundscapes of the Polish city of Białystok. It describes the progress made so far, including the selection of sonic landmarks, the process of acquiring the audio recordings, and the design of the unique graphic user interface featuring original drawings. Furthermore, it elaborates on the ongoing efforts to extend the project beyond the scope of a typical urban soundscape repository. In the present phase of the project, in addition to monophonic recordings, audio excerpts are acquired in binaural and Ambisonic sound formats, providing listeners with an immersive experience. Moreover, state-of-the-art machine-learning algorithms are applied to analyze gathered audio recordings in terms of their content and spatial characteristics, ultimately providing prospective users of the sound map with some form of automatic audio tagging functionality.
ER -